1 / 31

Project report

Project report. Automated image analysis of genome-wide RNAi microscopy assays Oleg Sklyar work with Florian Fuchs*, Gregoire Pau, Christoph Budjan*, Michael Boutros*, Wolfgang Huber * German Cancer Research Centre, Heidelberg, Germany. RNA interference (RNAi) gene silencing.

nizana
Download Presentation

Project report

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Project report Automated image analysis of genome-wide RNAi microscopy assays Oleg Sklyar work with Florian Fuchs*, Gregoire Pau, Christoph Budjan*, Michael Boutros*, Wolfgang Huber * German Cancer Research Centre, Heidelberg, Germany

  2. RNA interference (RNAi) gene silencing An unknown gene showing phenotypic similarities to a set of known genes taking part in one and the same process is likely to share their function or be part of the same process. Challenge: how to compare phenotypes given as images in an automated manner?

  3. Studying effects of gene silencing HeLa cells end-point assay - after 48h stain 3 channels: Actin (TRITC)‏ Tubulin (Alexa 488)‏ DNA (Hoechst)‏ BD Pathway, 4 sites per well, two scans; 172,000 images à 1 MegaPixel, 12 bit 22K genes x 2 replicates x 4 locations x 3 channels = 528K images 22K genes x 500 cells per gene (in all 8 images) = 11M cells

  4. Phenotypes Actin (TRITC)‏ Tubulin (Alexa 488)‏ DNA (Hoechst)‏

  5. Data analysis Image analysis – extraction of numeric features (image descriptors) from images and objects in images

  6. What is R? Why using R in this project? • R is a programming language and scripting environment for statistical computing and graphics. Aka FOSS S of Bell Labs or S+ of Insightful • very efficient on vectors, matrices, lists and data tables • extensive library of statistical routines • very effective in rapid prototyping, data analysis and hypothesis testing • extensible via packages, over 800 on CRAN, 200 on Bioconductor • very large developer base for statistical software • object oriented (if required)‏ • flexible interface to C/C++ and Fortran for fast calculations • available virtually on all architectures • frequent releases (half-year)‏

  7. The Bioconductor project is a cross-validated repository of R packages designed for bioinformatics and computational biology. • biology – a computational science: computational support and computational solutions for data analysis • complex but similar data structures: unification • availability of software to all biologists and labs • reproducible research requires open access to computational code • 29 core developers that develop and maintain the base packages; over 100 package developers

  8. EBImage – R package for image analysis • S4-based object-oriented R code • high-level R API for C/C++ code (.Call interface)‏ • cross-platform: UNIX/Linux, Win32, MacOS • GTK+ for cross-platform GUI • agile development framework: iterative, unit-based, complete units, frequent releases • 4500 lines of C/C++ code • 2000 lines of R code • 3200 lines of documentation • parts of code and testing by Mike Smith, Wolfgang Huber and Gregoire Pau • bug fixes, testing and ideas from R and Bioconductor communities

  9. Algorithms included Object feature extraction hull and edge features variants of image moments Zernike moments Haralick texture features Object manipulation object matching painting objects on images stacking and tiling objects Tools colour manipulations drawing primitives, annotation interactive display Extensive help for all routines Examples Use case vignette Image processing • image enhancement • noise reduction, blur, smoothing • image transformations Image analysis • segmentation, edge detection • morphological transforms • watershed segmentation • Voronoi-based segmentation R algorithms applicable to arrays • histograms • data subsetting, manipulation • Fast Fourier transforms • quantile, kurtosis, stat. tests, ...

  10. Image analysis workflow

  11. Finding nuclear envelopes (mask): segmentation with local adaptive threshold

  12. Distance map, watershed segmentation

  13. Voronoi diagrams on image manifolds • partition metric space with n seeds into n convex polygons such that • each polygon contains exactly one seed • every point in a polygon is closer to its seed than to any other • Seed sets: nuclei • Space: Cytoplasmic mask • Metric: Gradient limit =10-5 Euclidian limit=105 [Carpenter, Jones et al., CellProfiler]

  14. Analysis workflow

  15. Cell descriptors and classification

  16. Cell descriptors and classification

  17. Cell classification example

  18. Comparing images of single cells directly...

  19. Image query

  20. Data normalization Batch effects and the quality of controls Within-plate spatial trends

  21. The magnitude of effects

  22. Treatment effects?..

  23. Clustering genes by phenotype GO: Lipid metabolic process Oxidoreductases acting on the CH-OH group of donors with NAD+ or NADP+ as acceptor HSD11B2: Corticosteroid 11-beta-dehydrogenase isozyme 2 HSD3B7: 3 beta-hydroxysteroid dehydrogenase type 7 (MIPS, Classification of Enzymes)‏

  24. Distance • Assuming • Each gene i is described by a set of p descriptors • Distance d between gene i and j is parametric • Example • xik is the k-phenotype cell count observed on gene i • Weighted L2 distance: • Sigmoid-transformed coefficients + weighted L1 distance: • How to choose the parameters k,k and k ?

  25. Optimising distance over SPRING • STRING is a pairwise gene interactions database. • Idea: distance of genes picked on STRING should be lower in average than between random genes • Find the distance parameters k, k and k thatmaximize the separation between the distributions

  26. The controls

  27. Hits: probes far from NC

  28. Searching for small tight clusters

  29. Data browser, gene query

  30. Phenotype comparison

  31. Acknowledgements and references Wolfgang and the group for guidance, help and support R and Bioconductor developer communities EBI, DKFZ and HFSP (via a research grant to W. H. and M.B.) for financial support Rwww.r-project.org Bioconductorwww.bioconductor.org EBImagewww.ebi.ac.uk/~osklyar/EBImage (or Bioconductor)‏ imageHTSwww.ebi.ac.uk/~osklyar/imageHTS

More Related